Abstract
Fast and exact searching of digital image from the large database is the great demand. In this paper, a hybrid technique to improve the efficiency of content-based image retrieval (CBIR) is proposed. It uses combination of color, texture, and shape feature extraction methods. Color features are extracted using HSV histograms. For texture feature extraction, instead of traditional Gabor filter, four Gabor filters are simultaneously tuned in the desired direction using genetic algorithm and features are extracted in each direction simultaneously. The shape features are obtained using shape signature function with polygonal fitting algorithm. By the sequential process of these three stages, the retrieval performance is greatly improved. The simulation results prove that the proposed analysis gives significant improvement with respect to retrieval performance and computational complexity with the other proposed schemes.
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Madhavi, D., Ramesh Patnaik, M. (2018). Genetic Algorithm-Based Optimized Gabor Filters for Content-Based Image Retrieval. In: Singh, R., Choudhury, S., Gehlot, A. (eds) Intelligent Communication, Control and Devices. Advances in Intelligent Systems and Computing, vol 624. Springer, Singapore. https://doi.org/10.1007/978-981-10-5903-2_18
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DOI: https://doi.org/10.1007/978-981-10-5903-2_18
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